APT: Atomic Physical Transitions for Causal Video-Language Understanding

arXiv:2606.18586 · cs.CV, cs.AI · Submitted 2026-06-17 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-06-17

Updated: 2026-08-30

Code: https://github.com/isaac-sim/IsaacSim

Project page: https://polite-bombolone-d4250c.netlify.app

Terminology

• Trace — 41× in this paper · explained in 2 episodes
• Benchmark — 33× in this paper · explained in 2 episodes
• Atomic Physical Transition — 15× in this paper
• Grounding — 14× in this paper · explained in 3 episodes
• APEX — 11× in this paper · explained in 1 episode
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmarking — 5× in this paper · explained in 4 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Dynamical regimes — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Elias — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Institutional Review Board — 2× in this paper
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Vision-language models — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
• Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion
• PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models
• CLEVRER-Humans: Describing Physical and Causal Events the Human Way
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
• MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models
• Evaluating Newtonian Mechanics in Video Generative Models with Real Physical Systems

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models